In general, the larger an antenna, the greater the amount of information the antenna can obtain about a viewed object or area, and the more information the antenna obtains, the finer the resolution and more useful the imaging data becomes. Very large antennas are prohibitively expensive to place in orbit or on aircraft; however, researchers have learned to combine motion of a relatively small antenna with advanced signal processing techniques to simulate the results otherwise obtainable from only a very large antenna.
Synthetic Aperture Radar (SAR) antennas generate rapid radar pulses to image surface areas over which an aircraft or satellite carrying the SAR antenna is passing. The backscatter responses from the radar pulses are received by the SAR antenna and are interpreted with respect to phase and amplitude and recorded over a measured distance. By combining the backscatter responses from many pulses as the SAR passes overhead, a synthetic aperture is formed that is far larger than the aperture of the actual physical SAR antenna.
The SAR data is collected by the radar antenna as radio frequency analog data and converted to digital format through an analog-to-digital converter and provided to an image formation processor. The raw, unprocessed data is commonly referred to as Video Phase History (VPH) data. Each block of VPH data includes two components, an In-phase (I) component and a Quadrature (Q) component. As the VPH data is a waveform, the I is the wave's real component and the Q the imaginary component, with the I and Q combining to provide the wave's overall phase and magnitude.
The output of the image formation processor is detected image data which can be displayed as is, and complex image data which can be used as input to Coherent Change Detection and other complex SAR exploitation tools. This output data is transferred by a data link (e.g., radio frequency communications link) from the aircraft to the ground, where a ground based complex data exploitation processor at the ground station renders detected image or other information on a display.
The ultimate picture provided by SAR is composed of pixels—the smallest units of the picture composition. Not surprisingly, each pixel is therefore represented by the complex image data, and more specifically two data channels that deliver the phase and the magnitude components of the complex image. In addition, for each pixel of the complex image data displayed there is an associated Range and Azimuth corresponding to the physical location or facet scanned.
Advances in SAR technology have enabled the SAR sensors to collect increasingly large amounts of complex image data. The transmission of such data to ground stations typically involves a radio transmission having a limited frequency bandwidth. Moreover, the steady increase in collection efficiency and image size has raised the total data volume to the point where data latency is a problem and a limiting factor in the distribution of near real time information. When the imaged area corresponds to a disaster area, such as a coast line ravaged by a tsunami, or a combat zone, real time transfer and processing is key to the prevention of loss of life and property.
More specifically, at the present time each SAR pixel represented in complex image data is 64 bits, 32 bits each for the I and Q components. In certain SAR configurations, this information is encoded as phase and logarithm of magnitude with two hundred fifty six levels assigned to each. As a typical SAR image may include billions of pixels, the volume of data collected for transmission and processing is enormous. The United States Government has been funding investigations to solve the bandwidth and latency problems by focusing on compressing the transmitted pixels to a target of 5 bits per pixel without incurring image degradation.
Whereas, phase information of the image had been previously discarded, resulting in an immediate reduction in data by a factor of two, the newly recognized importance of phase data necessitates that it too be maintained and transmitted. Previous attempts to compress complex SAR data without degrading the information have not succeeded. Existing compression algorithms, such as JPEG, were specifically designed to take advantage of optical data statistics and high local data correlation.
More specifically, JPEG and other compression algorithms rely on statistical repetition of data, and the ability to represent a statistical group of pixels with a token representative. For example the image of a black ball on a red floor may be highly compressed because of the highly statistical nature of red and black elements within the image. If the red floor accounts for 95% of the image, a compression of well over 75:1 is easily achieved as for each red pixel there is a high local correlation to other red pixels. However, where the image data is perhaps best described as nearly white noise, and statistical elements are rare if even identifiable, the compression method falters as the statistical component is not substantially present.
Magnitude images generated from SAR Magnitude data do compress as the magnitude data generally has correlation. However, Phase data is highly uncorrelated and does not compress well using existing techniques. Several compression methods have been proposed to compress SAR data. While they generally work quite well for the Magnitude data, they do not efficiently compress the Phase data, because the compression methods, designed for visible electro optical imagery, rely on high local data correlation to achieve good compression results. When the data is left in I and Q form, neither component will compress well with JPEG type algorithms.
Complex SAR image data, either in I, Q or phase, magnitude form do not provide high rates of local data correlation. More simply stated, complex SAR data are highly non-statistical, and as such can not be effectively processed by statistical compression methods.
While various compression methods have been explored, each tends to apply unequally to either the magnitude or the phase component, which limits the later accuracy and usage of the compressed data. Remembering that the SAR gathering device is typically in an aircraft or satellite, the compression method employed also should involve minimal computational complexity so as to reduce the processing requirements available at the point of collection.
Hence, there is a need for a complex SAR data compression method to permit improved transfer from the gathering location to the image production location that overcomes one or more of the technical problems found in existing complex SAR data compression methods.